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README.md

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 <center> <b> Hawau Olamide Toyin, Samar Magdy, Hanan Aldarmaki </b> </center> 
 
  We investigate the effectiveness of large language models (LLMs) for text diacritization  in two typologically distinct languages: Arabic and Yoruba. To enable a rigorous evaluation, we introduce a novel multilingual dataset <strong>MultiDiac</strong>
- , with diverse samples that capture a range of diacritic ambiguities. We evaluate 14 LLMs varying in size, accessibility, and language coverage, and benchmarked them against 6 specialized diacritization models. Additionally, we fine-tune four small open-source models using LoRA for Yoruba. Our results show that many off-the-shelf LLMs outperform specialized diacritiztion models for both Arabic and Yoruba, but smaller models suffer from hallucinations. Fine-tuning on a small dataset can help improve diacritization performance and reduce hallucination rates.
+ , with diverse samples that capture a range of diacritic ambiguities. We evaluate 14 LLMs varying in size, accessibility, and language coverage, and benchmarked them against 6 specialized diacritization models. Additionally, we fine-tune four small open-source models using LoRA for Yoruba. Our results show that many off-the-shelf LLMs outperform specialized diacritiztion models for both Arabic and Yoruba, but smaller models suffer from hallucinations. Fine-tuning on a small dataset can help improve diacritization performance and reduce hallucination rates.
+
+
+ #### Cite this work:
+ @misc{toyin2025llmsgoodtextdiacritizers,
+      title={Are LLMs Good Text Diacritizers? An Arabic and Yor\`ub\'a Case Study}, 
+      author={Hawau Olamide Toyin and Samar M. Magdy and Hanan Aldarmaki},
+      year={2025},
+      eprint={2506.11602},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2506.11602}, 
+}